This patent includes a Computer Program Listing Appendix comprising the source code as found in the accompanying compact disc. The source code comprises the following files:
The source code as found in the computer program listing appendix and all the files therein, including the files listed above, is hereby incorporated by reference.
The present invention relates generally to object recognition, more specifically to computer based object recognition, and, more particularly, to a subspace morphing method for object recognition which obviates the need for normalization of images.
Although many successful techniques have been proposed in certain specific applications, object recognition, in general, remains a difficult and challenging problem. In appearance based approaches, representation of only the appearance of the object is still not sufficient for object recognition. This is because the variation space of the appearance is so huge that it is impossible to enumerate all the possible appearances (and then to index them). Depending upon the type of object at issue, the variation space comprises different variation dimensions. Existing techniques for object recognition require that the feature selection and matching be done in a predefined, dimensionally fixed space, and, if images of objects are given in different dimensions, a normalization in scale technique has to be applied to allow the feature vectors to fit into this space. Unfortunately, these normalization-based techniques have several major drawbacks, including, but not limited to an increase in processing time and resources.
In general, there are two types of variation dimensions in the appearance variation space. The first type of variation dimensions is due to the nature of the object itself, and different types of objects may result in different dimensions of variation. This type of variation dimension is referred to as an internal variation factor. In the case of a human face, for example, internal variation factors include face expression, face pose, face orientation, etc. The second type of variation dimensions is due to the environment and conditions when the object image is taken, and has nothing to do with the specific object represented by the image. This type of variation dimension is referred to as an external variation factor. Examples of external variation factors include scale (related to how close the camera is with respect to the object when the picture is taken), contrast (related to the lighting conditions when the picture is taken), and background (related to the environment where the picture is taken). External variation factors are independent of the object types to be recognized.
What is needed, then, is an object recognition method that does not require normalization of images.
The present invention is a computer-based method and apparatus for recognition of objects. The invention uses a novel subspace morphing theory which obviates the need for normalization of scales prior to identification.